Obuda University
Theses of Doctoral (PhD) Dissertation
Intelligent Decision Models
Author:
Orsolya Csiszar
Supervisors:
Prof. Dr. Janos Fodor
Prof. Dr. Robert Fuller
Doctoral School of Applied Informatics and Applied Mathematics
2015
Background 1
1. Background of the research: aggregation and decision
Aggregation is the process of combining several numerical values into a single represen-
tative one. The function, which performs this process is called an aggregation function.
Despite the simplicity of this definition, the size of the field of its applications is in-
credibly huge: applied mathematics (e.g. probability theory, statistics, decision theory),
computer sciences (e.g. artificial intelligence, operation research, pattern recognition
and image processing), economics and finance, multicriteria decision aid, etc. (see e.g.
Beliakov et al., 2007, Grabisch et al., 2009).
If we think of the arithmetic mean, we can see that the history of aggregation is as
old as mathematics itself. However, it was only in the last decades, when the rapid
development of the above mentioned fields (mainly due to the arrival of computers)
made it necessary to establish a sound theoretic basis for aggregation. The problem of
data fusion, synthesis of information or aggregating criteria to form overall decision is of
considerable importance in many fields of human knowledge. Due to the fact that data
is obtained in an easier way, this field is of increasing interest.
One of the most prominent group of applications of aggregation functions comes from
decision theory. Making decisions often leads to aggregating preferences or scores on a
given set of alternatives, the preferences being obtained from several decision makers,
experts, voters or representing different points of view, criteria and objectives. This
concerns decision under multiple criteria or multiple attributes, multiperson decision
making and multiobjective optimization (Fodor and Roubens, 1994).
Another outstanding application of aggregation functions comes from artificial intelli-
gence, fuzzy logic (Dubois et al., 2000). Pattern recognition and classification, as well
as image analysis are typical examples. According to Aristotle, in mathematics it was
originally assumed that ”the same thing cannot at the same time both belong and not
belong to the same object and in the same respect. [...] Of any object, one thing
must be either asserted or denied.” The idea of many-valued logic was initiated by Jan
Lukasiewicz around 1920. ”Logic changes from its very foundations if we assume that
in addition to truth and falsehood there is also some third logical value or several such
values” (Klement and Navara, 1999). Many-valued logic was for several decades con-
sidered as a purely theoretical topic. It was the introduction of fuzzy sets by Zadeh in
1965 (Zadeh, 1965), which opened the way to fuzzy logics.
Directions and Goals 2
2. Directions and goals of the research
Aggregation functions are inevitably used in fuzzy logic, as a generalization of logical
connectives. In artificial intelligence, these techniques are mainly used when a system has
to make a decision. It is possible that the system has not only a single criteria for each
alternative, but several ones. This case corresponds to a multicriteria decision-making
problem. Furthermore, if a system needs a good representation of an environment, it
needs the knowledge supplied by information sources in order to be reliable. However,
the information supplied by a single information source (by a single expert or sensor)
is often not reliable enough. That is why the information provided from several sensors
(or experts) should be combined to improve data reliability and accuracy and also to
include some features that are impossible to perceive with individual sensors.
In fuzzy modeling framework, the relationship of the input and the output can be mod-
eled by splitting the input into fuzzy regions for which we can describe the output in
different ways. In several applications, the roles of the inputs are not symmetric and
have different semantic contents. In this case, a proper construction of aggregation func-
tions is needed. To fulfil this requirement, a generation method of aggregation functions
from two given ones was examined in Chapter 2. The so-called threshold construction
method is based on an adequate scaling on the second variable of the initial operators.
The main factor in determining the structure of the needed aggregation function is the
relationship between the criteria. At one extreme there is the case in which we desire
all the criteria to be satisfied. At the other extreme is the situation in which we want
the satisfaction of any of the criteria. These two extreme cases lead to the use of ”and”
and ”or” operators to combine the criteria functions. A decision can be interpreted as
the intersection of fuzzy sets, usually computed by applying a t-norm based operator,
when there is no compensation between low and high degrees of membership. If it is
interpreted as the union of fuzzy sets, represented by a t-conorm based operator, full
compensation is assumed. However, it is obvious that no managerial decision represents
any of these extreme situations. As it is well-known, uninorms generalize both t-norms
and t-conorms as they allow for a neutral element neutral element anywhere in the unit
interval.
In Chapter 3, new construction methods of uninorms with fixed values along the borders
were discussed, and sufficient and necessary conditions were presented. These results
have theoretic importance in this research field.
One of the most significant problems of fuzzy set theory is the proper choice of set-
theoretic operations (Schweizel and Sklar, 1983, Weber, 1983). The class of nilpotent
t-norms has preferable properties which make them more usable in building up logical
Directions and Goals 3
structures. Among these properties are the fulfillment of the law of contradiction and
the excluded middle, or the coincidence of the residual and the S-implication (Dubois
and Prade, 1991, Trillas and Valverde, 1985). Due to the fact that all continuous
Archimedean (i.e. representable) nilpotent t-norms are isomorphic to the Lukasiewicz
t-norm (Grabisch et al., 2009, Klement et al., 2000), the previously studied nilpotent
systems were all isomorphic to the well-known Lukasiewicz-logic.
The new idea of using more than one generator functions gives us a chance to build
up a logical system in a significantly different way. To obtain a consistent nilpotent
system with the advantage of three naturally derived negations, we have thoroughly
examined the necessary and sufficient conditions in Chapter 4. In the case when the
natural negations do not coincide, we get a consistent nilpotent system, which is not
isomorphic to Lukasiewicz logic. The fixpoints of these natural negations can be used
for determining natural thresholds for different modifying words.
Our goal was to build up a logical system with the advantage of the nilpotent property
and the three naturally derived negations. To get an applicable system, we have thor-
oughly examined the negations, conjunctions, disjunctions, implications and equivalence
operators in bounded systems (Chapter 4-6).
Scientific Results 4
3. New scientific results
The thesis is concerned with the development of intelligent decision models from a
theoretical point of view. It covers two main topics: in Chapter 2-3, two special types of
aggregation functions are studied and results on new construction methods are presented,
while in Chapter 4-6, the so-called general nilpotent operator system is introduced and
examined.
1. Thesis Group I.
The properties of a new construction method of aggregation functions from two
given ones, called threshold construction, are discussed. This class of non-symmetric
functions provides a generalization of t-norms and t-conorms by partitioning the
unit interval with respect to only one variable. In fuzzy modeling framework, the
relationship of the input and the output can be modeled by splitting the input
into fuzzy regions for which we can describe the output in different ways.
(a) Thesis 1.1. The new type of aggregation function turned out to be
monotonic and continuous, having a right-neutral and idempotent ele-
ment. Three possible ways of symmetrizations are studied, two of them
using min-max operators and the third using uninorms. After proving
the lack of associativity in all cases, the bisymmetry and all the other
associativity-like equations known from the literature are studied.
(b) Thesis 1.2. New construction methods of uninorms with fixed values
along the borders are presented. Sufficient and necessary conditions are
presented.
Relevant own publications pertaining to this thesis group: [79, 80].
1. Thesis Group II.
In the second part of the thesis (Chapter 4-6), logical systems, more specifi-
cally, nilpotent logical systems are in consideration. It is shown that a consistent
logical system generated by nilpotent operators is not necessarily isomorphic to
Lukasiewicz-logic. This new type of nilpotent logical systems is called a bounded
system, which has the advantage of three naturally derived negations. Implication
and equivalence operators in bounded systems are deeply examined and a wide
range of examples is also presented.
Scientific Results 5
(a) Thesis 2.1.
The concept of a nilpotent connective system is introduced. It is shown
that a consistent logical system generated by nilpotent operators is not
necessarily isomorphic to Lukasiewicz-logic, which means that nilpotent
logical systems are wider than we have thought earlier. Using more than
one generator functions, three naturally derived negations are examined.
It is shown that the coincidence of the three negations leads back to a
system which is isomorphic to Lukasiewicz-logic. Consistent nilpotent
logical structures with three different negations are also provided.
(b) Thesis 2.2.
Necessary and sufficient conditions for the classification property (the
excluded middle and the law of contradiction), the De Morgan law and
consistency have been given.
(c) Thesis 2.3.
Both R- and S-implications with respect to the three naturally derived
negations of the bounded system are considered. It is shown that these
implications never coincide in a bounded system, as the condition of co-
incidence is equivalent to the coincidence of the negations, which would
lead to Lukasiewicz logic. The formulae and the basic properties of four
different types of implications are given, two of which fulfill all the basic
properties generally required for implications. A wide range of examples
is also presented. The concept of a weak ordering property is defined.
Two different implications, ic and id are introduced, both of which fulfill
all the basic features generally required for implications.
(d) Thesis 2.4.
A detailed discussion of equivalence operators in bounded systems are
given. Three different types of operators are studied. After taking a closer
look at the implication-based equivalences, the properties of the so-called
dual equivalences are studied. Using these two types of equivalence opera-
tors, a new concept of aggregated equivalences is introduced. The paradox
of the equivalence relation is solved by aggregating the implication-based
Scientific Results 6
equivalence and its dual operator. It is shown that the aggregated equiv-
alence possesses nice properties like threshold transitivity, T-transitivity
and associativity. For applications in image processing, the overall equiv-
alence of two grey level images was defined, and an important semantic
meaning of the aggregated equivalences is given.
Relevant own publications pertaining to this thesis group: [81, 82, 83, 84, 85].
Practical Applicability 7
4. Practical applicability
In fuzzy modeling framework, the relationship of the input and the output can be mod-
eled by splitting the input into fuzzy regions for which we can describe the output in
different ways. In several applications, the roles of the inputs are not symmetric and
have different semantic contents. In this case, the introduced threshold construction of
aggregation functions has great importance.
By examining nilpotent logival systems, our goal was to build up a consistent system
with the advantage of the nilpotent property and the three naturally derived negations.
The fixpoints of these natural negations can be used for determining natural thresholds
for different modifying words. To get a widely applicable system, we have thoroughly
examined the negations, conjunctions, disjunctions, implications and equivalence opera-
tors in bounded systems. For applications in image processing, the overall equivalence of
two grey level images was defined, and an important semantic meaning of the aggregated
equivalences was given.
The main disadvantage of the Lukasiewicz operator family is the lack of differentiability,
which would be necessary for numerous practical applications. Although most fuzzy
applications (e.g. embedded fuzzy control) use piecewise linear membership functions
due to their easy handling, there are significant areas, where the parameters are learned
by a gradient based optimization method. In this case, the lack of continuous derivatives
makes the application impossible. For example, the membership functions have to be
differentiable for every input in order to fine tune a fuzzy control system by a simple
gradient based technique.
This problem could be easily solved by using the so-called squashing function (see Dombi
and Gera, 2008), which provides a solution to the above mentioned problem by a con-
tinuously differentiable approximation of the cut function. This approximation could
provide the next step along the path to a practical and widely applicable system, with
the advantage of three naturally derived negation operators.
——————————————————————–
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